# Data Analysis with Bayesian Networks: A Bootstrap Approach

@article{Friedman1999DataAW, title={Data Analysis with Bayesian Networks: A Bootstrap Approach}, author={Nir Friedman and Mois{\'e}s Goldszmidt and Abraham J. Wyner}, journal={ArXiv}, year={1999}, volume={abs/1301.6695} }

In recent years there has been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in complex data analysis problems, we need to go beyond being satisfied with inducing networks with high scores. We need to provide confidence measures on features of these networks: Is the existence of an edge between two nodes warranted? Is the Markov blanket of a given node robust? Can we say something about the ordering of the variables? We should be able to…

## 329 Citations

### Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks

- Computer ScienceMachine Learning
- 2004

This paper shows how to efficiently compute a sum over the exponential number of networks that are consistent with a fixed order over network variables, and uses this result as the basis for an algorithm that approximates the Bayesian posterior of a feature.

### bnstruct : an R package for Bayesian Network Structure Learning with missing data

- Computer Science
- 2016

The bnstruct package provides objects and methods for learning the structure and parameters of the network in various situations, such as in presence of missing data, for which it is possible to perform imputation (guessing the missing values, by looking at the data), or the modeling of evolving systems using Dynamic Bayesian Networks.

### Bagged Structure Learning of Bayesian Networks

- Computer Science
- 2011

A novel approach for density estimation using Bayesian networks when faced with scarce and partially observed data, and replaces the standard model selection score by a bootstrap aggregation objective aimed at sifting out bad decisions during the learning procedure.

### Bagged Structure Learning of Bayesian Network

- Computer ScienceAISTATS
- 2011

A novel approach for density estimation using Bayesian networks when faced with scarce and partially observed data, and replaces the standard model selection score by a bootstrap aggregation objective aimed at sifting out bad decisions during the learning procedure.

### Constraint-based structural learning in Bayesian networks using finite data sets

- Computer Science
- 2001

This thesis focuses on the constraint-based approach for those cases where only a finite amount of data is available, as typical in practical applications and among various extensions to this approach the so-called necessary path condition is presented.

### Learning Complex Bayesian Network Features for Classification

- Computer ScienceProbabilistic Graphical Models
- 2006

The probabilistic semantics of such statements, the computational challenges and possible solutions of Bayesian inference over complex Bayesian network features, particularly over features relevant in the conditional analysis are discussed.

### Learning the structure of Bayesian Networks via the bootstrap

- Computer ScienceNeurocomputing
- 2021

### Bayesian network structural learning from complex survey data: a resampling based approach

- Computer ScienceStatistical Methods & Applications
- 2022

A modified version of the PC algorithm is proposed for inferring causal structure from complex survey data based on resampling techniques for finite populations and the robustness with respect to departures from the assumptions is shown.

### Learning Bayesian networks: approaches and issues

- Computer ScienceThe Knowledge Engineering Review
- 2011

This work takes a broad look at the literature on learning Bayesian networks—in particular their structure—from data, and hopes that all the major fields in the area are covered.

### Learning Bayesian networks: approaches and issues

- Computer Science
- 2011

This work takes a broad look at the literature on learning Bayesian networks—in particular their structure—from data, and aims to locate all the relevant publications.

## References

SHOWING 1-10 OF 24 REFERENCES

### A Tutorial on Learning with Bayesian Networks

- Computer ScienceInnovations in Bayesian Networks
- 1998

Methods for constructing Bayesian networks from prior knowledge are discussed and methods for using data to improve these models are summarized, including techniques for learning with incomplete data.

### On the application of the bootstrap for computing confidence measures on features of induced Bayesian networks

- Computer ScienceAISTATS
- 1999

This paper takes a well-known method from statistics, Efron’s Bootstrap, and examines its applicability for assessing a confidence measure on features of the learned network structure and compares it to assessments based on a practical realization of the Bayesian methodology.

### Learning Bayesian Networks is NP-Complete

- Computer ScienceAISTATS
- 1995

It is shown that the search problem of identifying a Bayesian network—among those where each node has at most K parents—that has a relative posterior probability greater than a given constant is NP-complete, when the BDe metric is used.

### A Guide to the Literature on Learning Probabilistic Networks from Data

- Computer ScienceIEEE Trans. Knowl. Data Eng.
- 1996

The literature review presented discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic…

### An Introduction to the Bootstrap

- Economics
- 1993

Statistics is the science of learning from experience, especially experience that arrives a little bit at a time. The earliest information
science was statistics, originating in about 1650. This…

### CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP

- EconomicsEvolution; international journal of organic evolution
- 1985

The recently‐developed statistical method known as the “bootstrap” can be used to place confidence intervals on phylogenies and shows significant evidence for a group if it is defined by three or more characters.

### A Transformational Characterization of Equivalent Bayesian Network Structures

- Computer ScienceUAI
- 1995

A simple characterization of equivalent Bayesian network structures based on local transformations is presented, able to easily prove several new invariant properties of theoretical interest for equivalent structures.

### Probabilistic reasoning in intelligent systems - networks of plausible inference

- Computer ScienceMorgan Kaufmann series in representation and reasoning
- 1989

The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.

### Array of hope

- BiologyNature Genetics
- 1999

The genome sequences have not only made a new era of exploration imperative, but, providentially, they have also made it possible to take a fresh, comprehensive and open-minded look at every question in biology.